利用PACE OCI确定陆地生态系统总初级生产力

IF 4.4
Karl Fred Huemmrich;Skye Caplan;John A. Gamon;Petya Krasteva Entcheva Campbell
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引用次数: 0

摘要

利用浮游生物,气溶胶,云,海洋生态系统(PACE)海洋颜色仪器(OCI)的数据开发和测试远程检索陆地生态系统生产力的算法。根据代表美国植被和气候变率的47个涡动相关通量塔的CO2通量计算了总初级生产力(GPP)。8天平均GPP与包含从紫外到短波红外光谱区49个光谱波段的8天平均OCI反射率数据进行匹配。这些数据涵盖了从2024年3月到9月的生长季节。对于所有地点和日期的组合,红边叶绿素指数单独描述了66%的GPP变化。采用偏最小二乘回归(PLSR)对各光谱波段的GPP检索提高到74%。在这些回归中,经常发现农业场所具有高残留。通过生态气候区域对PLSR进行训练,总体GPP检索率提高到86%。这些算法在不同植被类型和生长季节的多个站点上的成功,证明了PACE OCI数据在大陆尺度上绘制GPP动态的效用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determining Terrestrial Ecosystem Gross Primary Productivity From PACE OCI
Data from the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Ocean Color Instrument (OCI) were used to develop and test algorithms for remotely retrieving terrestrial ecosystem productivity. Gross primary productivity (GPP) was calculated from CO2 flux for 47 eddy covariance flux towers representing vegetation and climatic variability across the USA. Eight-day average GPP was matched with eight-day average mapped OCI reflectance data containing 49 spectral bands from ultraviolet through short wave infrared spectral regions. The data covered the growing season from March through September 2024. For the combination of all sites and dates, the red-edge chlorophyll index alone described 66% of the variation in GPP. Using a partial least squares regression (PLSR) on all spectral bands GPP retrieval was improved to 74%. Agricultural sites were often found to have high residuals in these regressions. By training PLSR by eco-climatic region, the overall GPP retrievals were improved to 86%. The success of these algorithms across multiple sites with different vegetation types and through the growing season demonstrates the utility of PACE OCI data to map GPP dynamics at continental scales.
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